The applications of deep neural networks to sdBV classification

TL;DR

This paper explores the use of deep neural networks, specifically ANN and CNN, for classifying synthetic data of pulsating stars, demonstrating high accuracy with minimal hyperparameter tuning to aid large-scale astrophysical surveys.

Contribution

It introduces a method for applying deep learning to astrophysics by generating synthetic data and shows both ANN and CNN can classify this data effectively.

Findings

Deep learning models can classify synthetic pulsating star data accurately.

Both ANN and CNN paradigms are effective for this classification task.

High accuracy achieved with minimal hyperparameter tuning.

Abstract

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and we show that two separate paradigms of deep learning - the Artificial Neural Network And the Convolutional Neural Network - can both be used to classify this synthetic data effectively. And that additionally this classification can be performed at relatively high levels of accuracy with minimal time spent adjusting network hyperparameters.